s2fieldsofstudy: Biology Medicine

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Genetic analysis of cancer drivers reveals cohesin and CTCF as suppressors of PD-L1

Significance The PD-1/PD-L1 immunoinhibitory axis plays a key role in immune evasion of cancer cells, and therapies targeting PD-1/PD-L1 show high efficacy in certain cancer types. Understanding how cancer drivers regulate immune surveillance will enable development of novel therapeutic strategies targeting cancer-mediated immune evasion, and identification of new biomarkers of response. Here, we utilized genetic screening with a curated library of 500 tumor suppressor genes to identify cohesin subunits and CTCF among the most significant suppressors of PD-L1. We report upregulation of additional key immune-regulatory molecules PD-L2 and MHC-I, and describe transcriptional consequences of loss of the STAG2 cohesin subunit, including an induction of IFN and NF-κB responses, which have implications for biology and treatment of cohesin-deficient tumors.

Immune evasion is a significant contributor to tumor evolution, and the immunoinhibitory axis PD-1/PD-L1 is a frequent mechanism employed to escape tumor immune surveillance. To identify cancer drivers involved in immune evasion, we performed a CRISPR-Cas9 screen of tumor suppressor genes regulating the basal and interferon (IFN)-inducible cell surface levels of PD-L1. Multiple regulators of PD-L1 were identified, including IRF2, ARID2, KMT2D, and AAMP. We also identified CTCF and the cohesin complex proteins, known regulators of chromatin architecture and transcription, among the most potent negative regulators of PD-L1 cell surface expression. Additionally, loss of the cohesin subunit RAD21 was shown to up-regulate PD-L2 and MHC-I surface expression. PD-L1 and MHC-I suppression by cohesin were shown to be conserved in mammary epithelial and myeloid cells. Comprehensive examination of the transcriptional effect of STAG2 deficiency in epithelial and myeloid cells revealed an activation of strong IFN and NF-κB expression signatures. Inhibition of JAK-STAT or NF-κB pathways did not result in rescue of PD-L1 upregulation in RAD21-deficient cells, suggesting more complex or combinatorial mechanisms at play. Discovery of the PD-L1 and IFN up-regulation in cohesin-mutant cells expands our understanding of the biology of cohesin-deficient cells as well as molecular regulation of the PD-L1 molecule.
cancer j cohesin j PD-L1 j CRISPR C ancer cells acquire genetic and epigenetic changes that promote escape from normal tissue homeostasis and enable uncontrolled growth. In addition to impaired differentiation, increased proliferation, and prosurvival properties, cancer cells can acquire the ability to evade the immune system (1). Mechanisms of immune evasion commonly acquired by cancer cells include but are not limited to suppression of T-cell function and infiltration, down-regulation of antigen presentation, recruitment of immunosuppressive cell types, and release of immunosuppressive cytokines (2). Detection of virally infected and cancerous cells by CD8+ T cells occurs through the interaction between the T-cell receptor and antigens presented on MHC molecules on the surface of target cells. Antigen recognition leading to activation of CD8+ T cells is accompanied by up-regulation of inhibitory receptors such as PD-1 (programmed death-1), which, when engaged by cognate ligands such as PD-L1 (programmed death-ligand 1) on target cells, prevents immune overreaction by limiting the activity of CD8+ T cells. In this way, inhibitory receptors, also known as checkpoint molecules, prevent tissue damage and help maintain selftolerance (3,4). CD8+ T cells secrete type II interferon, interferon gamma (IFN-γ), and regulate antigen presentation as well as expression of immunoinhibitory ligands. These opposing responses are thought to have differential kinetics that allow an appropriate modulation of response during infection. IFN-γ is the most potent PD-L1 inducer in the cancer microenvironment (5,6). Activation of JAK-STAT signaling downstream of the IFN receptor IFNGR2 results in up-regulation of IRF1, which binds directly to the PD-L1 promoter inducing its expression (6). IRF2 is a basally expressed competitive repressor of IRF1, which negatively regulates PD-L1 (7,8). PD-L1 expression can also be induced by type I interferons, IFN-α and IFN-β, albeit to a lesser degree (6). Cancer cells exploit this off switch by up-regulating expression of immunoinhibitory molecules such as PD-L1 and PD-L2. Expression of PD-L1 on cancer cells and immune infiltrates drives immune escape in a context-dependent manner (9)(10)(11). PD-L1/ PD-1 checkpoint blockade has been shown to be a highly effective treatment strategy in a specific subset of tumor types, and PD-L1 levels and presence of immune infiltrates act as predictors of response to PD-L1/PD-1 checkpoint blockade therapies in certain contexts (12,13).
Understanding how cancers activate immune evasion could provide additional insights into new therapeutic strategies to

Significance
The PD-1/PD-L1 immunoinhibitory axis plays a key role in immune evasion of cancer cells, and therapies targeting PD-1/ PD-L1 show high efficacy in certain cancer types. Understanding how cancer drivers regulate immune surveillance will enable development of novel therapeutic strategies targeting cancer-mediated immune evasion, and identification of new biomarkers of response. Here, we utilized genetic screening with a curated library of 500 tumor suppressor genes to identify cohesin subunits and CTCF among the most significant suppressors of PD-L1. We report upregulation of additional key immune-regulatory molecules PD-L2 and MHC-I, and describe transcriptional consequences of loss of the STAG2 cohesin subunit, including an induction of IFN and NF-κB responses, which have implications for biology and treatment of cohesin-deficient tumors. circumvent cancer-mediated immune evasion along with biomarkers that could aid application of those therapeutic strategies. Some of the mechanisms by which tumor cells may activate immune evasion are likely driven by cancer driver mutations themselves. Here, we perform an unbiased CRISPR-Cas9 screen of basal and inducible PD-L1 expression levels using human mammary epithelial cells (HMECs) with a curated single-guide (sgRNA) library targeting 500 tumor suppressor genes. We identify the cohesin complex-related components and CTCF, known regulators of three-dimensional chromatin organization and gene expression, as negative regulators of PD-L1 cell surface expression. Our comprehensive analysis of gene expression changes induced by deficiency of the cohesin subunit STAG2 reveals an induction of strong IFN and NF-κB responses. Our findings thus have significant implications for understanding genetic regulators of PD-L1 expression as well as biology of cohesin-mutant cells.

Results
Genetic Screen Identifies Subunits of the Cohesin Complex and CTCF as Strong Negative Regulators of PD-L1. Given the role of the immune system in shaping cancer evolution, we hypothesized that mutations in a subset of frequently mutated cancer drivers would promote immune evasion. Therefore, to identify regulators of basal and IFN-γ-inducible PD-L1 expression in cancer, we performed a CRISPR-Cas9 genetic screen utilizing a tumor suppressor gene (TSG) sgRNA library. This library contains 10 sgRNAs targeting each of the top 500 genes whose mutational patterns in tumors are suggestive of a possible role as a TSG by the Tumor Suppressor and Oncogene Explorer (TUSON) algorithm (14). We used HMECs expressing hTERT, given their high basal level of PD-L1 expression inducible by IFN-γ treatment (SI Appendix, Fig. S1A) in our screen. HMECs infected with the TSG CRISPR library were stained with an anti-PD-L1 antibody and sorted into three bins based on PD-L1 expression (PD-L1 low [bottom 7%], PD-L1 middle, and PD-L1 high [top 7%]) ( Fig. 1 A and B). To identify regulators of inducible IFN-γ expression, we also performed the screen in the presence of IFN-γ, where we treated HMECs infected with the TSG library with IFN-γ for 24 h (Fig. 1C). We hypothesized that sgRNAs targeting negative regulators of PD-L1 expression, such as IRF2, would be enriched in the PD-L1 high-expressing cells while sgRNAs targeting positive regulators of PD-L1 expression, such as components of the JAK-STAT pathway, would be enriched in the PD-L1 lowexpressing cells.
We identified known positive and negative regulators of PD-L1 basal and inducible cell surface expression. Components of the JAK-STAT signaling pathway present in the TSG library, including the IFN-γ receptor IFNGR2 and JAK1 kinase, were depleted in the PD-L1 high bin compared to the PD-L1 middle bin only in the presence of exogenous IFN-γ ( Fig. 1 B and C). We also identified that IRF2 is the most potent suppressor of PD-L1 in the TSG library in both the presence and absence of IFN-γ ( Fig. 1 B and C). Moreover, IRF2BP2, a negative regulator of IRF2, was found to positively regulate PD-L1 expression ( Fig. 1 B and C). The screen thus revealed components of the IFN-γ signaling pathway and known regulators of PD-L1 downstream of IFN-γ signaling as the most significant regulators of basal and inducible PD-L1 cell surface expression, as previously reported. Several previously unknown negative regulators of PD-L1 expression were identified, including the extracellular protein AAMP, subunit of the PBAF chromatin remodeling complex ARID2, and histone methyltransferase KMT2D ( Fig.  1 B and C).
Among the previously unappreciated genes scoring in this screen were all three cohesin complex-related proteins present in the TSG library, including the cohesin ring subunits STAG2 and RAD21 and the cohesin-loading protein NIPBL, as well as CTCF, which marks the boundaries of cohesin-mediated loop extrusion. The cohesin ring is a multimeric protein complex formed by SMC3, SMC1, RAD21, and the partially redundant STAG2 and STAG1 proteins that play several essential cellular functions, including sister chromatid cohesion, chromatin organization, transcriptional regulation, and DNA damage repair (15,16). NIPBL and MAU2 are components of the cohesinloading complex necessary for cohesin loading onto DNA (15). Mutations in cohesin ring subunits and modulators have been found to be recurrent genetic drivers across multiple cancer types, including myeloid malignancies and breast cancer (17).
Cohesin and CTCF genes all behaved as potent negative regulators of PD-L1 surface levels in the presence and absence of IFN-γ ( Fig. 1 B-E and SI Appendix, Fig. S1B). RAD21, NIPBL, STAG2, and CTCF all scored among top 15 hits in the presence of IFN-γ ( Fig. 1 B and C). As RAD21, NIPBL, and CTCF are essential genes, hypomorphic alleles of these genes were likely generated by CRISPR. This is consistent with the fact that cohesin subunits in tumors are generally heterozygous predicted loss-of-function mutations exhibiting haploinsufficiency, except for STAG2, which is an X-linked gene and has a partially redundant paralog STAG1 (17,18). Altogether, our CRISPR-Cas9 screen of basal and inducible regulators of PD-L1 cell surface expression utilizing a TSG library has identified multiple TSGs which regulate PD-L1 cell surface expression. Interestingly, we identify an unexpected role of CTCF and the cohesin complex proteins as some of the most significant suppressors of PD-L1 cell surface expression.
Loss of the Cohesin Subunits and CTCF Results in Up-regulation of PD-L1 Cell Surface Expression. To validate negative regulation of PD-L1 by the components of the cohesin ring and CTCF, we generated loss-of-function mutations of individual cohesin ring subunits STAG2 and RAD21, as well as CTCF, using CRISPR-Cas9 in HMEC cells. Efficacy of CRISPR-Cas9-mediated reduction of CTCF, RAD21, and STAG2 protein levels was confirmed via Western blot analysis (SI Appendix, Fig. S2A). Mutation of STAG2, RAD21, and CTCF resulted in up-regulation of PD-L1 surface expression relative to the negative control sgRNA targeting the AAVS1 locus ( Fig. 2A). Since CTCF and RAD21 are essential genes, we also utilized short interfering RNA (siRNA)-mediated depletion to transiently deplete CTCF, STAG2, and RAD21 messenger RNA (mRNA). siRNA-mediated depletion of RAD21, STAG2, and CTCF recapitulated the findings of CRISPR-mediated knockout (KO) in HMECs ( Fig. 2 B-E and SI Appendix, Fig. S2 B and C). The increase in PD-L1 cell surface expression in cohesin-depleted cells was accompanied by PD-L1 protein up-regulation (Fig.  2G). CTCF depletion had a weaker PD-L1 up-regulation phenotype than STAG2 or RAD21 depletion in both CRISPR-Cas9-mediated mutation and siRNA-mediated depletion experiments, suggesting that the cohesin ring may play a more important role in PD-L1 regulation than CTCF.
The STAG2 subunit of the cohesin complex is the most frequently mutated subunit of cohesin, while mutations in its paralog, STAG1, are not frequently found in cancer (17,18). STAG1-and STAG2-bound cohesin have many overlapping functions including sister chromatid cohesion and a few distinct roles (16,19). We were thus interested in whether STAG2 and STAG1 share the function of suppressing PD-L1 expression. Interestingly, STAG1 deficiency did not result in up-regulation of PD-L1 (Fig. 2F). PD-L1 up-regulation by STAG2 deficiency, but not STAG1 deficiency, suggests that PD-L1 suppression is a selective function of STAG2-bound cohesin in these cells.
To determine whether this regulatory circuit is conserved in other tissue types, we next examined different models of myeloid malignancies in which cohesin mutations are common genetic drivers of transformation (20). Mutations in the components of the cohesin complex and its modulators occur in ∼13% of patients with de novo acute myeloid leukemia (AML) and 20% of patients with high-risk myelodysplastic syndromes (MDS) and secondary AML (17)(18)(19)(20)(21)(22). We examined the levels of PD-L1 expression in two different AML cell lines that were engineered with CRISPR-Cas9 technology with heterozygous mutations in SMC3 and RAD21 and homozygous mutations in STAG2 (SI Appendix,      Wild-Type2 Wild-Type3  S2H). Mutation of STAG1 did not influence PD-L1 expression in this screen, which is consistent with our results for STAG1 depletion in HMECs (Fig. 2F). Altogether, these data suggest that the STAG2-cohesin complex acts as a negative regulator of basal and inducible PD-L1 expression in different tissue types.

Loss-of-Function Cohesin Mutations Result in Up-regulation of IFN
Response Gene Signatures in Breast and Myeloid Cells. To comprehensively examine the transcriptional response to loss of cohesin proteins, we performed RNA-sequencing (RNA-seq) analysis on wild-type (WT) and STAG2-mutant U937 and STAG2 siRNAdepleted HMECs. Hundreds of differentially expressed genes were identified (Fig. 3A). Gene set enrichment analysis (GSEA) of the Hallmark gene sets revealed multiple up-regulated pathways in both cell lines (Fig. 3B). Several Hallmark gene sets involved in immune-related functions were identified (Fig. 3B). IFN-α and IFN-γ signatures were the top two most significantly enriched gene sets in both cell types (Fig. 3 B-F and SI Appendix, Fig. S3 A and B). Several components of the IFN-signaling pathway, IFN response factors (IRFs), and IFN-stimulated genes (ISGs) were found to be up-regulated in STAG2-deficient HMECs and STAG2-mutant U937 cells (Fig. 3D). Several ISGs were found to be shared between STAG2-deficient epithelial and myeloid cells, but there were also tissue-specific differences in IRF utilization and ISG induction (Fig. 3F). PD-L1 was found to be transcriptionally up-regulated in the RNA-seq analysis of STAG2-deficient HMECs (Fig. 3G), suggesting that PD-L1 up-regulation in cohesin-deficient cells is at least partially transcriptional. Although the levels of PD-L1 expression in IFN-γ-untreated U937 cells are very low, loss of STAG2 resulted in transcriptional PD-L1 up-regulation (Fig.  3H). Similarly, PD-L1 mRNA levels are up-regulated in RAD21-depleted HMECs (SI Appendix, Fig. S3C). These results suggest that PD-L1 is regulated by cohesin deficiency in at least a partially transcriptional manner. The degree of transcriptional up-regulation of PD-L1 likely does not account for the total protein-level and cell surface-level up-regulation, and we suspect that there are other nontranscriptional mechanisms of PD-L1 up-regulation in cohesin-deficient cells.
Up-regulation of ISGs has many protective roles against pathogens and tumorigenesis. One of the key roles of IFN stimulation is up-regulation of class I histocompatibility gene (MHC-I) expression to enhance antigen presentation and T-cell killing of target cells. We were therefore interested to see if cohesin-mutant cells up-regulate MHC-I. HLA genes were found to be transcriptionally up-regulated in RNA-seq of STAG2-deficient HMEC and STAG2-mutant U937 cells (Fig.  3I). We examined cell surface levels of MHC-I in U937 and K562 AML cells in the presence and absence of IFN-γ in WT, SCM3, or STAG2-mutant cells. While we observed mild effects of cohesin mutations on basal MHC-I expression, there was a significant increase of MHC-I expression in cohesin-mutant cells in the presence of IFN-γ (Fig. 3 J and K and SI Appendix, Fig. S3 D and E). We similarly observed MHC-I up-regulation in RAD21-depleted HMECs (SI Appendix, Fig. S3F). Furthermore, loss of RAD21 and STAG2 in the presence of IFN-γ led to increased expression of PD-L2, a second immunoinhibitory ligand of PD-1 (SI Appendix, Fig. S3G). We thus identified up-regulation of the IFN-α signaling pathway in cohesinmutant cell lines as well as up-regulation of several key targets of IFN signaling, including PD-L1, MHC-I, and PD-L2.
STAG2-Deficient Cells Activate JAK-STAT and NF-κB Pathways, Which Do Not Contribute to PD-L1 Up-regulation. We next explored possible regulators of transcriptional activation of immune genes in cohesin-deficient cells. RNA-seq analysis in STAG2-deficient cells revealed activation of the hallmark NF-κB transcriptional targets (Fig. 3B). This was corroborated by activation of the NF-κB pathway as assessed by p65 phosphorylation and activation of the NF-κB luciferase reporter (consisting of a minimal promoter containing five NF-κB binding motifs upstream from firefly luciferase) in RAD21-siRNA-depleted HMECs (Fig. 4 A and B) as well as STAG2and SMC3-mutant U937 cells (Fig. 4D). NF-κB pathway activation has been reported to result in transcriptional and posttranslational PD-L1 up-regulation in several cell types (24,25).
To determine if the NF-κB pathway is required for PD-L1 upregulation in response to cohesin deficiency, we inhibited the pathway by expressing the mutant "superrepressor" IκBα allele in HMECs. The IκBα superrepressor harbors two amino acid substitutions (S32A/S36A), which prevent its phosphorylation and degradation and thus inhibit nuclear translocation of NF-κB. We confirmed that expression of the mutant IκBα superrepressor prevented nuclear translocation of NF-κB upon TNF-α treatment (SI Appendix, Fig. S4A). However, mutant IκBα did not suppress PD-L1 up-regulation in RAD21-deficient HMECs (Fig. 4C), suggesting that activation of the NF-κB pathway in RAD21-deficient HMECs does not contribute to PD-L1 up-regulation.
We next examined the role of the JAK-STAT signaling pathway in activating IFN-stimulated gene expression. Cohesin-mutant AML cells significantly up-regulate p-STAT1 levels, consistent with activation of the JAK-STAT pathway (Fig. 4D). We confirmed protein up-regulation of several IFN-inducible factors including IRF7 and ISG15 in STAG2-and SMC3-mutant U937 cells (Fig. 4D). RNA-seq analysis revealed increased transcriptional levels of IFN-β transcripts in STAG2-mutant U937 cells, a potential source for the activation of JAK-STAT signaling (SI Appendix, Fig. S4B). Additionally, we utilized an interferonstimulated response element (ISRE) luciferase reporter, consisting of a minimal promoter with five ISRE motifs upstream of the luciferase gene, to measure activation of interferon response factors in HMECs (Fig. 4E). ISRE motifs are recognized by interferon response factors whose transcription is induced by activation of JAK-STAT signaling downstream of the IFN receptor. We detected increased reporter activity in RAD21-deficient HMECs stably expressing the ISRE luciferase reporter, confirming activation of IRFs in cohesin-deficient HMECs (Fig. 4E).
Type I and type II IFNs and downstream activation of JAK-STAT signaling are known positive regulators of PD-L1 expression. To test whether activation of JAK-STAT signaling is responsible for PD-L1 up-regulation in STAG2-deficient cells, we treated RAD21-depleted cells with the JAK inhibitor ruxolitinib. Treatment with ruxolitinib suppressed induction of PD-L1 expression in the presence of IFN-γ, but it did not prevent PD-L1 up-regulation in RAD21-deficient HMECs (Fig.  4F). These data suggest that activation of JAK-STAT signaling does not drive up-regulation of PD-L1 expression in RAD21deficient HMECs. Consistent with this observation, we did not observe up-regulation of the key downstream PD-L1 regulator IRF1 (6) or IRF8 in cohesin-deficient HMECs or cohesinmutant AML cells (SI Appendix, Fig. S4C). We suspect that the failure to induce IRF1 might explain why strong IFN response does not seem to contribute to PD-L1 up-regulation across our cohesin-deficient AML and HMEC models.
Altogether, our data demonstrate strong activation of JAK-STAT and NF-κB signaling in STAG2-deficient and RAD21deficient breast epithelial and myeloid cell lines. Intriguingly, inhibition of the JAK/STAT or NF-κB pathways does not rescue PD-L1 up-regulation in RAD21-deficient cells, suggesting that PD-L1 induction might be independent of IFN and NF-κB activation in the context of RAD21 deficiency.

Discussion
In this study, we performed a CRISPR-Cas9 screen with a curated sgRNA library targeting tumor suppressor genes to identify negative regulators of PD-L1 expression. Unexpectedly, we identified CTCF and multiple cohesin genes, STAG2, RAD21, and NIPBL, as the top negative regulators of IFN-γ-inducible PD-L1 up-regulation within the library of the 500 most frequently mutated tumor suppressor genes. STAG2 deficiency was previously reported to up-regulate PD-L1 levels in U2OS cells (26), although whether this was due to its role in cohesion or a distinct function of STAG2 had not been clear. Here, we identify additional subunits of the cohesin complex and CTCF as negative regulators of PD-L1 through unbiased screening and strongly implicate the cohesin complex in regulating PD-L1 expression levels. PD-L1 regulation by the cohesin complex is at least in part transcriptional. The ability of the cohesin ring to extrude loops of DNA has been found to play an important role in shaping the three-dimensional organization of the genome (27,28). The cohesin complex and CTCF play an essential role in the generation and maintenance of topologically associated domains that regulate gene expression by facilitating pairing between promoters and their cognate enhancers (15,29). CTCF has no known role in sister chromatid cohesion but phenocopies regulation of PD-L1 expression by cohesin, which suggests that their shared roles in chromatin organization and regulation of gene expression likely affect PD-L1 regulation. It remains to be elucidated whether cohesin and CTCF loss directly impacts the transcriptional regulation of PD-L1 locus or whether it has an indirect effect on transcription of a PD-L1 regulator.
Deficiency of individual cohesin ring subunits has previously been reported to up-regulate or down-regulate IFN signaling in different cellular models (30,31).We comprehensively analyzed the transcriptional impact of STAG2 deficiency in primary breast and myeloid cancer cell lines and detected up-regulation of type I IFN response genes. However, several canonical IFN response genes were instead found to be down-regulated by cohesin deficiency despite a strong activation of IFN signature. These downregulated factors include IRF8 and IRF1, key positive regulators of IFN-inducible transcriptional programs. Down-regulation of a number of IFN response genes could perhaps be explained by the previously reported necessity of the cohesin complex for transcriptional activation of certain IFN-inducible genes. Recently, loss of Rad21 in primary mouse macrophages was shown to lead to rapid transcriptional down-regulation of IFN response genes, suggesting a necessity for the cohesin complex for the transcriptional activation of certain subsets of ISGs (31). Based on these observations, we hypothesize that cohesin deficiency results in IFN response induction in epithelial and  myeloid human cell lines, but the necessity of the cohesin complex for transcriptional up-regulation of certain IFN response genes, such as IRF1 and IRF8, results in their down-regulation. We speculate that this might result in a "noncanonical" IFN response phenotype with unique consequences on the tumor microenvironment of cohesin-deficient cells. Recurrent somatic mutations in genes encoding cohesin subunits and modulators have been identified across a wide spectrum of human malignancies, and STAG2 is one of only a dozen human genes found to be significantly mutated in four or more distinct cancer types (32). Our previous analysis of mutations in PanCancer The Cancer Genome Atlas (TCGA) datasets revealed that there is a strong selective pressure for loss-of-function mutations in CTCF and STAG2 (14). CTCF ranked as the 20th, and STAG2 as the 29th, most potent TSG based on distortion of the mutational signature toward loss-of-function mutations. In support of our observations that CTCF and cohesin play roles in regulating immune-related phenotypes, we have recently identified CTCF as one of the most potent regulators of immune evasion in both triple-negative breast cancer and colon cancer cell lines using in vivo CRISPR screening in WT and immunodeficient mice (33). Future studies will be needed to confirm the role of loss of CTCF and cohesin components in cancer immune evasion in vivo, but we speculate that up-regulation of JAK-STAT signaling, NF-κB pathway, and PD-L1 expression will play a key role. While these mechanisms remain to be elucidated, the discovery of the PD-L1 and IFN up-regulation in cohesin-mutant cells expands our understanding of transcriptional regulation of PD-L1 as well as the biology of cohesin-deficient cells, both of which may have important implications for our understanding of biology and treatment of cohesin-deficient tumors.
CRISPR-Cas9 Screen of Regulators of PD-L1 Cell Surface Expression. A library of ∼5,000 sgRNAs targeting the top 500 TSGs with 3,000 sgRNA controls was designed as previously described (35). Cells were transduced with lentivirus containing the CRISPR TSG library in duplicate at multiplicity of infection (MOI) of 0.2 and representation of 1,000 cellular integrations per sgRNA. After selection in 3 μg/mL puromycin for 3 d and expansion for 2 d, cells were treated with 10 ng/mL IFN-γ (Invitrogen no. PHC4031) or phosphate buffered saline (PBS) for 24 h, followed by an additional treatment with 10 ng/mL IFN-γ or PBS 1 h prior to harvesting cells. Cells were then stained with an allophycocyanin (APC)-conjugated CD274 monoclonal antibody (eBioscience, clone MIH1) at final concentration of 1.5 μg/million cells, fixed with BD Cytofix Fixation Buffer (Fisher catalog no. 554655), subjected to fluorescence activated cell sorting (FACS) at FACSAria Flow Cytometer (BD Biosciences), and pelleted for analysis. Gates were set so that the PD-L1 high and PD-L1 low populations received ∼7% of stained cells each. For the PD-L1 + IFN-γ screen, four populations were collected from sorting: PD-L1 high , PD-L1 med , PD-L1 low , and PD-L1 verylow . Gates were again set so that the PD-L1 high and PD-L1 low populations received ∼7% of stained cells each. The PD-L1 verylow contained ∼3% of cells and was excluded from further analysis.
Analysis of Screen Results. Genomic DNA was isolated from cell pellets by four rounds of phenol:chloroform extraction using Phaselock tubes (5 PRIME) and three rounds of chloroform extraction. A total of 25 μg/mL Rnase A (Qiagen catalog no. 19101) was added and incubated for 4 h at 37°C. DNA was ethanol precipitated, recovered by centrifugation, washed three times with 70% ethanol, and resuspended in 10 mM Tris-Cl pH 8.5.
sgRNA barcode sequences were PCR amplified from resuspended genomic DNA and adapted for Illumina NextSeq500 sequencing. Reads were aligned to the reference library using Bowtie (36). CRISPR read counts were analyzed by Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout (MAGeCK) to calculate gene rank lists, P values, and false discovery rates (FDRs) for each gene (37). Results of MAGeCK analysis of the high versus middle bin in the PD-L1-IFNγ screen are available in Dataset S1. Results of MAGeCK analysis of the high versus middle bin in the presence of IFN-γ screen are available in Dataset S2.
To construct luciferase reporter plasmids, the complementary DNA (cDNA) for Renilla luciferase followed by a T2A was synthesized as a gene block (Integrated DNA Technologies). The Renilla-T2A sequence was cloned into pHAGE DEST PGK Blast (38) via Gibson assembly to yield pHAGE DEST PGK Renilla-T2A-Blast. Next, the NF-κB-TA-firefly luciferase sequence, a gift from DarrellKotton, Boston University, Boston, Massachusetts (Addgene plasmid catalog no. 49343) (39), was cloned into pHAGE DEST PGK Renilla-T2A-Blast via Gibson assembly to generate the NF-κB reporter pHAGE NF-κB-TA-firefly luciferase PGK Renilla-T2A-Blast. The IFN-stimulated response element (ISRE) reporter was made by annealing complementary oligos containing five ISRE motifs flanked by NheI and NcoI sites followed by ligation into the pHAGE NF-κB reporter backbone digested with NheI and NcoI. Plasmids were verified by Sanger sequencing.
Lentiviral Transduction and CRISPR-Cas9-Mediated Knockout in HMECs. Virus was prepared by transfecting 293T cells with the sgRNA lentiCRISPR version 2 plasmid and psPax2 and pMD2.G lentiviral packaging vectors. Viral supernatants were harvested at 48 h posttransfection. The amount of virus that resulted in ∼30% cell viability (MOI ∼0.3) following puromycin selection was used for infection to ensure low MOI. Since knockouts of CTCF and RAD21 are lethal, cell pellets were harvested 48 h after selection. Efficacy of CRISPR-Cas9-mediated mutation for reducing protein levels was confirmed via Western blotting.
The same methodology was used for lentiviral production and transduction of ISRE and NF-κB luciferase reporters and WT and superrepressor IκBα constructs. After transduction with IκBα constructs, mAmetrine positive cells were FACS sorted.
Western Blotting. All cells were lysed in radioimmunoprecipitation assay (RIPA) lysis buffer with protease and phosphatase inhibitors (Thermo catalog no. 78441). Protein was quantified by Bradford assay (Bio-Rad). Protein lysates were separated on 4 to 12% Bis-Tris gels (Invitrogen catalog no. NP0335BOX) and transferred to nitrocellulose membranes (Bio-Rad). Membranes were blocked with 5% milk in Tris-buffered saline with Tween 20 (TBS-T) (Cell Signaling catalog no. 997S) and incubated with the following antibodies overnight at 4°C RNA-seq Library Preparation and Sequencing. For U937 cells, total RNA was isolated from 1 million cells using the RNeasy Plus Isolation Kit (Qiagen) following the manufacturer instructions. The library was prepared using the Tru-Seq mRNA kit (Illumina) according to the standard protocol. Sequencing was performed on HiSeq 4000 using 100-bp paired-end reads, and 50 million reads were obtained per sample. For HMECs, total RNA was isolated from ∼1 million cells using the RNeasy Plus Isolation Kit (Qiagen) following the manufacturer instructions. RNA-seq was performed on two replicates of cells treated with no siRNA, two replicates treated with control siRNA #1, two replicates with control siRNA #2 and three replicates each of cells treated with siRNA STAG2 #2 and siRNA STAG2 #3. The library was prepared using the NEBnext Ultra II Directional RNA Library kit according to the standard protocol from 1 μg isolated RNA. Barcoded libraries were pooled at molar ratios, and 86-bp singleend sequencing was performed using Ilumina NextSeq 500.
RNA-seq Data Processing. For U937, RNA-seq reads were mapped using STAR version 2.4.2a. The STAR genome index was generated from human reference sequence hg19 (FASTA downloaded from the University of California, Santa Cruz genome browser) and coding transcripts from GENCODE Release 19 (GTF downloaded from https://www.gencodegenes.org/). Reads mapping to genes were quantified with featureCounts from the R package Subread (version 2.0.1). Transcripts per million (TPM) were quantified from the count data. The sequencing reads and processed TPM are available on Gene Expression Omnibus (GEO) (accession no. GSE189818). For HMECs, RNA-seq reads were mapped using STAR version 2.7.0a to the hg19 genome (41). Reads mapping to genes were quantified with featureCounts. Raw read counts were analyzed using DESeq2 (42) GSEA. A matrix of TPM values was used as the input for GSEA with three replicates per genotype for U937 cells and six replicates for HMECs. Pathway enrichment in the STAG2-mutant U937 and STAG2-deficient HMECs was determined for the Hallmark (C1) gene sets. A summary table of the normalized enrichment score and FDR for each of the hallmark gene sets is included in Datasets S3 and S4. U937 and K562 Cell IFN-γ Treatment and Flow Cytometry. U937 and K562 cells were treated with 2,000 IU/mL of human IFN-γ for 72 h (PeproTech catalog no. 300-02). A total of 500,000 cells were collected per condition and washed with FCS buffer (PBS + 2% FBS). Staining was performed with 1:100 anti-PDL1 (PE) (BioLegend catalog no. 329706) and 1:100 anti-MHC1 (FITC) (BioLegend catalog no. 311404). Isotype controls used were mouse IgG2b, k-PE (BioLegend catalog no. 400313), and mouse IgG2a, k-FITC (BioLegend catalog no. 400208). Cells were washed twice after staining and analyzed immediately on a BD LSR Fortessa. Analysis of cell populations and MFI calculation were performed in FlowJo.
Immunofluorescence and Microscopy. Approximately 20,000 HMECs expressing IκBα WT or IκBα superrepressor were grown in tissue culture chamber slides. Cells were treated with 20 ng/mL TNF-α (PreproTech catalog no. 300-01A) for 20 min prior to fixation. Cells were then washed twice with PBS and fixed for 15 min at room temperature in 4% paraformaldehyde (PFA) (Electron Microscopy Science catalog no. 15713). Cells were permeabilized with 0.5% Triton-X for 10 min at room temperature. Cells were blocked in 3% bovine serum albumin (BSA) (Amresco catalog no. 0332). Primary antibody against p65 (Cell Signaling catalog no. 8242) was diluted 1:200 in 3% BSA and incubated for 1 h at room temperature. Secondary antibody was diluted 1:500 in 3% BSA and incubated for 1 h at room temperature. Slides were mounted with DAPI Fluoromount-G (Southern Biotech catalog no. 0100-20). Slides were imaged on a wide-field Nikon Ti inverted microscope.
Luciferase Reporter Assays. Approximately 1 million HMEC cells expressing ISRE and NF-κB reporters were passively lysed, and Firefly luciferase and Renilla luciferase activity was analyzed using the Dual-Luciferase Reporter Assay System (Promega catalog no. E1960) following the manufacturer's instructions. Each sample was run with three technical replicates. Firefly luciferase activity was normalized to Renilla luciferase activity, which is driven by the PGK constitutive promoter.
Statistical Analysis. The significance was analyzed using one-way ANOVA with post hoc test or Student's t test. Differences were considered significant when P <0.05.
Data Availability. RNA-seq data are available on the Gene Expression Omnibus (GEO), https://www.ncbi.nlm.nih.gov/geo/ (accession: GSE189818) (43). All other study data are included in the article and/or supporting information.


Discovery of Novel Quinoline-Based Proteasome Inhibitors for Human African Trypanosomiasis (HAT)

Human African Trypanosomiasis (HAT) is a vector-borne disease caused by kinetoplastid parasites of the Trypanosoma genus. The disease proceeds in two stages, with a hemolymphatic blood stage and a meningo-encephalic brain stage. In the latter stage, the parasite causes irreversible damage to the brain leading to sleep cycle disruption and is fatal if untreated. An orally bioavailable treatment is highly desirable. In this study, we present a brain-penetrant, parasite-selective 20S proteasome inhibitor that was rapidly optimized from an HTS singleton hit to drug candidate compound 7 that showed cure in a stage II mouse efficacy model. Here, we describe hit expansion and lead optimization campaign guided by cryo-electron microscopy and an in silico model to predict the brain-to-plasma partition coefficient Kp as an important parameter to prioritize compounds for synthesis. The model combined with in vitro and in vivo experiments allowed us to advance compounds with favorable unbound brain-to-plasma ratios (Kp,uu) to cure a CNS disease such as HAT.

■ INTRODUCTION
Human African Trypanosomiasis (HAT), also known as African sleeping sickness, is a devastating neglected tropical disease caused by protozoa parasites of the Trypanosoma brucei (Tb) genus and transmitted by the Tsetse fly (Glossina genus). 1−4 The disease proceeds in two stages. In the first stage, the parasites multiply in the subcutaneous tissue, the blood, and the lymphatic system. 3−5 In the second stage, the parasites cross the blood−brain barrier (BBB) to invade the central nervous system (CNS). 6,7 At this stage, patients typically display disturbances of the sleep cycle, which gives the disease its name. African sleeping sickness is fatal if untreated. Gambiense HAT infections can often linger for months or even years without symptoms and become chronic. 3 Patients are often in an advanced CNS stage of the disease when symptoms emerge. Two subspecies of the parasite are observed in the field with T. brucei gambiense accounting for 97% of the reported cases and T. brucei rhodiense for the remaining 3%. 1−3 The most recent epidemic started in 1970 and lasted until the late 1990s. 1, 3 In 2019, the WHO published new guidelines for the treatment of sleeping sickness with the approval of 10-day oral dosing of fexinidazole. 8,9 Prior to the approval of fexinidazole, the standard of care as of 2009 was oral nifurtimox combined with an IV infusion of eflornithine. 10 Tropical disease drug discovery is challenging and very few parasite drug targets have been validated, yet some breakthroughs have emerged for the treatment of sleeping sickness. 11−13 A few interesting approaches have been reported elsewhere. 14−16 The 20S proteasome core consists of four stacked rings arranged in αββα-configuration. Each ring consists of seven different subunits with β1, β2, and β5 being the catalytically active subunits that cleave cellular proteins selected for degradation. 17,18 The proteasome was previously demonstrated to be a validated target for combatting parasitic diseases including HAT, Chagas disease, and Leishmaniasis. 19−21 The compounds discussed here are T. b. brucei-specific chymotrypsin-like proteasome inhibitors, which are binding at the interface of the β4 and the β5 subunit with more than 1000× selectivity against the human proteasome. For all compounds presented in this paper which we profiled in a biochemical assay on the human proteasome, we measured activities of >10 μM (details in the Supporting Information).

■ RESULTS AND DISCUSSION
Our starting point was compound 1, which was discovered as a singleton hit from a biochemical HTS screen with 3 million compounds against Leishmania tarentolae 20S proteasome. This compound showed promising inhibition of the T. b. brucei chymotrypsin activity. Compound 1 not only had <1 μM cellular potency against T. b. brucei but also an attractive in vitro ADME profile, particularly the high passive permeability and low P-gp efflux as assessed by MDCK-MDR1, which struck us as an ideal starting point for a compound requiring brain penetration. After rapidly screening different tertiary and secondary amines, we noted a steep increase in activity with the latter (e.g., compound 2) (Table 1). Table 1. SAR (Biochemical and Cellular) and ADME Properties in Quinoline and Naphthyridine Series a a We could rationalize the improvement through the use of computational modeling and the subsequent cryo-EM structure of compound 2 bound with L. tarentolae 20S proteasome. The newly introduced N−H (R4) is masked in an intramolecular Hbond, retaining the desired permeability properties, while at the same time restricting the conformation of the tail-piece (R3) to fit into the channel pocket of the binding site. The main interactions responsible for the potency were hypothesized to be the aniline N−H interaction with Tyr113 and noncovalent molecular interactions including π-stacking of the quinoline aromatic system with Phe24 ( Figure 1).
Upon identifying secondary amides as more active, we screened a variety of different amide tail-pieces (R3/R4, Tables  1 and 4). The initially explored aliphatic carbocycles suffered from low metabolic stability. We therefore increased the polarity of the side chain, leading us to explore aminopropanediolderived tail-pieces. This allowed us to identify compounds with improved activity and stability while maintaining a favorable efflux profile (e.g., compound 3). Based on that initial success, we investigated derivatives such as aminofluoropropanols and aminomethoxypropanols (e.g., compounds 4−7). We next turned our attention to the impact of different head-pieces (R2), as the stereo-electronic properties of the aniline should have a dramatic effect on potency due to the involvement of this vector in the primary protein interaction responsible for activity. We found that electron-poor anilines, particularly those containing an ortho-fluorine, greatly improved potency as well as metabolic stability. By introducing an additional nitrogen to the quinoline core leading to a 1,7-naphthyridine, we could improve solubility and liver microsome clearance in all species (compare compounds 3 and 11). 1,5-and 1,8-Naphthyridines suffered from loss of activity (see the Supporting Information). The introduction of a basic amine into the amide side chain of a 1,7-naphthyridine led to compound 10, which displayed improved activity and balanced in vitro ADME properties. Due to our initial success with aminopropanediol-derived tail-pieces in the quinoline scaffold, we attempted to transfer the SAR to the 1,7-naphthyridine core. Unfortunately, this led to a decrease in activity, low permeability, and high efflux as seen in compound 11. Figure 1. Cryo-EM structure of compound 2. The atomic model has been deposited in the Protein Data Bank with the accession code PDB ID 7ZYJ. The 260,000 particles in the best two-dimensional (2D) classes were used for three-dimensional (3D) refinement using C2symmetry. The resulting refinement of best particles gave rise to a reconstruction with an overall resolution of 2.8 Å.  The application of the cryo-EM structure allowed us to identify the minimum pharmacophore. We found that vinyl-and ethoxy-pyridines show good activity and balanced physicochemical properties (compounds 13−15, Table 2). Although the pyridines were identified as the minimal pharmacophore required for T. b. brucei growth inhibition, we could not achieve single-digit nanomolar potency. Moreover, these compounds showed moderate to high liver microsomal clearance, indicating potential issues with in vivo pharmacokinetics. Interestingly, all compounds from the series displayed high permeability and low efflux, likely due to their lower molecular weight and smaller size.
The most promising compounds were selected for mouse brain pharmacokinetics (PK), plasma protein binding (PPB), and brain tissue binding (BTB) measurements to determine the total and unbound brain-to-plasma ratio (K p and K p,uu ). In keeping with the 3R principles with respect to animal welfare (Reduction, Refinement, Replacement), we sought an in silico model capable of predicting K p to minimize the number of animal experiments required to identify a compound with satisfactory brain penetration. Initially, we found that a number of in silico models, including the central nervous system multiparametric optimization (CNS MPO) model, 22,23 did not correlate with our experimental results. However, when we employed a model provided through StarDrop 24 we found better qualitative correlation between the predicted log BB (blood-to-brain ratio) and the experimentally measured K p (r 2 = 0.91) 25 for a variety of compounds across different chemotypes (see Figure 2 and Table 3). The model allowed us to predict K p prior to synthesis to prioritize compounds with higher predicted log BB values (i.e., log BB − 0.1, which translated to experimental K p > 0.3 in mouse in our initial validation set), which helped rapid SAR generation of compounds in the desired property space.
After investigating potency, in vitro ADME properties and determining K p,uu for compounds in the quinoline, naphthyridine, and pyridine series, we selected the most promising leads for PK studies in rodents (Tables 1−4). We found that all compounds generally had low to moderate clearance and that the in vivo clearance in the mouse was indeed higher than that in the rat, as predicted by in vitro liver microsomes (Table 5). Compounds 5, 6, and 7 had the best in vivo profiles in both rodent species, exhibiting low clearance and good oral exposures with good to excellent oral bioavailability at low doses. However, they also exhibited high plasma protein binding in mouse with free fractions of <1%. Due to the limitations of the PPB and the BTB assays with compounds that show >99% binding, we advanced tool compounds with <99% binding to avoid additional challenges with respect to establishing in vitro−in vivo correlation (IVIVC) for unbound clearance (Cl u ) and PKPD for unbound brain-to-plasma ratios (K p,uu ) required to achieve complete clearance of parasites. As a result, compound 4 Details on the biochemical and cellular assay as well as on clearance (Cl int ) and permeability (MDCK-MDR1) can be found in the Experimental Section. Figure 2. Correlation of log BB and measured K p (@ 5 min time point), red: initial set of compounds to build model, black: compounds prioritized based on model. 25 was initially selected to establish PKPD for the quinoline scaffold.
Therefore, we evaluated compound 4 in a stage I HAT mouse efficacy model, where mice were infected with T. b. brucei STIB975 and treated from day 3 to day 6 after the infection was established. 26−29 Compound 4 showed full cure at 3 and 10 mg/ kg (mpk) bid as well as 10 mpk qd. At the lowest dose of 1 mpk bid, four out of six animals were cured (Table 6). Compounds 5, 6, and 7 from the quinoline series were subsequently evaluated at 3 mpk qd and with the exception of 5 all achieved complete cure of the stage I infection at this low dose (Table 6). However, compounds from the naphthyridine series showed partial cure only at higher doses of 10 mpk bid (data not shown). We hypothesized that higher doses were required due to the lower potency and therefore higher concentrations needed to be achieved to see a parasiticidal effect. Despite displaying good PK properties in mouse, compounds such as 9 were not able to achieve and maintain the required concentrations for cure for a long enough period of time. We were also able to demonstrate sterile cure with a compound from the pyridine series (e.g., compound 14) at 30 mpk bid with 4 d dosing. Due to 14 being 5-fold more potent than 9, lower concentrations were required.
However, compound 14 is 4-fold less potent compared to 4 and displays inferior mouse PK with lower exposures, requiring bid dosing and higher doses to achieve cure (see the Supporting Information).
With stage I data in hand, we set out to predict curative doses for the stage II efficacy model. Animals were infected with T. b. brucei and allowed for infection to reach the brain. Treatment started from day 21 to day 27 post-infection. 26−29 It was hypothesized that we could use K p,uu to translate the results from our stage I study to the stage II study. Our model compound 4 showed a dose-dependent response. While we did not see cure with 10 mpk bid, we were able to cure one animal at 30 mpk qd, two animals at 60 mpk qd, and ultimately all six animals with 150 mpk qd (Table 7). Compounds 5 and 6 also achieved full cure at 100 mpk qd doses. To our delight, our frontrunner compound 7 achieved cure at a dose of only 15 mpk bid in the CNS model, likely due to superior potency as well as PK properties.
Further in vitro safety profiling established that the 6-methyl substituent on the quinoline ring of compound 4 had genotoxic potential in the TK6 in vitro mammalian cell gene mutation assay, making it undesirable for further development. Although the 6-bromo compound 5 was negative in the absence of rat liver a Solubility: Miniaturized shake flask solubility as described in the Experimental Section; Cl int assay described in the Experimental Section; f u,plasma from PPB; LM scaled Cl int = (LM Cl int ·SF1·SF2)/1000 with SF1 (mg protein per g liver) m,r = 45, SF2 (g liver per kg BW); m = 87.5, SF2 r = 40. Unbound clearance: Cl u = Cl p /f u , with Cl p observed clearance. Mouse and rat PK 1 mpk IV/5 mpk PO. *(5, 7, 9 PK for 1 mpk IV and 3 mpk PO).  S9, a positive signal resulted in the presence of rat liver S9. Fortunately, 6-chloro quinolines 6 and 7 did not show this genotoxic potential (Table 8). During the lead optimization campaign, we also monitored the hERG binding and automated patch clamp (q-patch) signal to address any proarrhythmic potential of the compounds linked to the inhibition of this cardiac ion channel. We found that the key to minimizing binding was the 6-substituent on the quinoline. While methyl-, bromo-, and iodo-substituents showed a higher risk for hERG inhibition, hERG binding as well as inhibition potential was attenuated by fluoro-and chloro-substituents. Compounds, in general, represent high risk for ventricular arrhythmias if the safety margin (EC 50 /free C max ) for the hERG channel is less than 30. 30 Considering the free C max in mouse at the efficacious dose, compound 7 showed the best safety profile with respect to a potential hERG liability (Table 8) and was therefore chosen to be progressed into a 7-day oral gavage in vivo pilot toxicology study. In this study, the bone marrow was identified as a target organ of toxicity but only at the highest dose tested (300 mg/kg/ day). The exposure multiples between the efficacious dose (15 mg/kg bid) and the rat TK at 100 and 300 mg/kg are approximately 5-fold and 15-fold, respectively. In summary, we have identified and characterized a selective 20S proteasome inhibitor to combat human African trypanosomiasis. The program started out with a singleton HTS hit that exhibited superior physicochemical properties required for brain penetration. Guided by a cryo-EM structure of an active compound bound to the proteasome, we were able to rapidly optimize the potency of candidate compounds. We employed a StarDrop model to help predict the brain-to-plasma partition coefficient K p and used these data to select compounds for synthesis, PK and efficacy studies. We ultimately identified a candidate molecule 7, that cured a stage II mouse model of HAT. Therefore, we believe that this molecule has the potential to be a clinical candidate for African sleeping sickness.

■ EXPERIMENTAL SECTION
All materials and reagents used were of the best commercially available grade and used without further purification. Normal-phase column chromatography was carried out using prepacked silica gel cartridges on a Combiflash Rf separation system by Teledyne ISCO. 1 H NMR spectra were determined on a Varian 400 or Bruker 300 or 400 and 500 MHz NMR spectrometers. The following abbreviations are used: s = singlet, d = doublet, dd = doublet of doublets, t = triplet, q = quartet, m = multiplet, bs = broad singlet. Preparative HPLC was performed on a Waters Prep HPLC system using a C18 reversed-phase column eluting with gradient mixtures of water/acetonitrile containing a modifier 0.05% trifluoroacetic acid. HPLC analysis showed that all compounds were >95% pure. The purity of the final compounds was confirmed by UPLC-MS and UPLC. Additionally, all final compounds presented with in vivo data have been confirmed by 1 H NMR, LCMS, and HRMS in the Supporting Information.
High-resolution images were collected with a Cs-corrected FEI Titan Krios TEM operated at 300 kV equipped with a Quantum-LS Gatan Image Filter (GIF) and recorded on a K2-Summit direct electron detector (Gatan GmbH). Images were acquired automatically (with EPU, Thermo Fisher) in electron-counting mode (nominal post-GIF magnification of ×130,000 and calibrated pixel size of 0.86 Å). Exposures of 7 s were dose-fractionated into 40 frames. The total exposure dose was ∼40 e − /Å 2 . Defocus values varied from −0.8 to −2.5 μm.
Micrographs were drift-corrected using UNBLUR 33 before estimating CTF parameters using CTFFIND4. 34 Particle picking was carried out using cisTEM. 35 Picked particles were extracted into boxes of 300 × 300 pixels. Micrographs with severe drift or ice contamination were discarded based upon inspection of the power spectra. A total of 7000 micrographs were acquired from which 1 million particles were extracted for processing using CisTEM software package. 35 The 260,000 particles in the best 2D classes were used for 3D refinement using C2-symmetry. The resulting refinement of best particles gave rise to reconstruction with an overall resolution of 2.8 Å. The resolution values reported are based on the gold standard Fourier shell correlation curve (FSC) at 0.143 criterion. The cryo-EM structures of 20S proteasome (PDB ID code 6TD5) were manually fitted into the final cryo-EM map using the program Coot. 36 The resultant atomic model was subjected to multiple cycles of model rebuilding using the program Coot and real space refinement against the map using the program Phenix. 37 This process resulted in an atomic model of the proteosome− compound 2 complex that fit well into the cryo-EM density. Structural illustrations were prepared with PyMOL (www.pymol.org).
Biological Profiling. High-Throughput Screening. Leishmania torantolae parasites were grown to log phase, and cells were harvested by centrifugation and stored at −80°C freezer. 20S proteasome was purified using the protocol described earlier. 20,32 The purified proteasome was used for single-point chymotrypsin activity inhibition screen using 3 million compound library. Chymotrypsin activity was measured using a fluorescent probe as described earlier. Hits obtained were further confirmed in dose−response assay.
Chymotrypsin Inhibition Activity. 20S proteasome was isolated from T. brucei brucei (T. b. brucei) Lister 427 by following protocols described earlier. 20 Chymotrypsin inhibition activity of compounds was measured using rhodamine-labeled fluorogenic chymotrypsin substrate by incubating varying concentrations of compounds with T. b. brucei 20S proteasome. IC 50 was determined using GraphPad Prism software.
Parasite Growth Inhibition Studies. Growth inhibition studies for all compounds were carried out using bloodstream form of T. b. brucei Lister 427 strain as described earlier. 21,29 Briefly, 10-point threefold dilutions of compounds with starting concentration of 50 μM were added into 384-well white plates. These plates were incubated for 48 h with 1 × 10 4 /mL of T. brucei Lister 427 parasites grown using Hirumi-9 media supplemented with 10% FBS and 10% serum plus. Cell Titer Glo was added to measure the ATP levels as a surrogate for cell viability by quantifying the luminescence. The EC 50 values were determined using HELIOS software.
In Vivo Pharmacokinetic Analysis. In vivo pharmacokinetics (PK) for compounds were generated using BALB/c mice and Wistar rats using standard procedure described elsewhere. 21 For intravenous PK studies compounds were dosed at 1 mg/kg dose formulated in 75% 2.5 mg/mL PEG300 and 25% D5W (5% dextrose in distilled water).
For oral PK studies, compounds were dosed at 5 mg/kg formulated using a simple suspension of 0.5% v/v methyl cellulose tween 80. Each group had three animals, blood samples were collected at six time points post dosing, and compound levels were monitored using LCMS-MS. Various PK parameters were calculated using WIONLIN software. Brain-to-plasma ratio determination was carried out using mice dosed intravenously with 1 mg/kg of compounds, and blood and brain samples were collected at post 5 min and 60 min. Compound concentration was determined using the LCMS-MS method. All of the in-life studies were carried out under protocols approved by IACUC (Institutional animal care and use committee), following animal ethics guidelines of NITD, Emeryville, CA.
Mouse Efficacy Studies. Efficacy of compounds to eradicate T. b. brucei infection in mice models of human African trypanosomiasis were carried out using protocols described elsewhere. 21,29 Stage I Mouse Efficacy. Briefly, for hemolymphatic mice model (Stage I), NMRI mice were infected with bloodstream form of T. b. brucei STIB975 strain. Three days post infection, the mice were orally gavaged for 4 days with varying concentration of test compound. Each group of compound treatment had six mice that were monitored for blood parasitemia over a period of 31 days. At the end of 30 days, if the mice were clear of parasites, they were considered as cure. In vivo efficacy studies in mice were conducted at the Swiss Tropical and Public Health Institute (Basel) (License number 2813), according to the rules and regulations for the protection of animal rights ("Tierschutzverordnung") of the Swiss "Bundesamt fur Veterinarwesen". They were approved by the veterinary office of Canton Basel-Stadt, Switzerland.
Stage II Mouse Efficacy. For meningoncephalic mice model (stage II), CD1 mice were infected with bioluminescent strain of T. b. brucei GVR35 and allowed for 21 days for the infection to reach the brain. On day 21, a group of six mice were treated orally with varying concentration of test compound for a period of 7 days. The mice were monitored weekly for parasitemia relapse using IVIS imaging system for bioluminescence. The mice were considered cured at the end of 90 days post infection, if no bioluminescence was detected. All animal procedures were undertaken in adherence to experimental guidelines and procedures approved by The Home Office of the UK government. All work was covered by Home Office Project Licence PPL60/4442 entitled "Molecular Genetics of Trypanosomes and Leishmania". All animal protocols received approval from the University of York and University of Glasgow Ethics Committees.
Determination of Microsomal Stability, Permeability, Plasma Protein Binding, and Brain Tissue Binding. Intrinsic metabolic clearance for compounds was determined in mouse, rat, and human liver microsomes using the compound-depletion method and LCMS-MS quantification. 38 Permeability for compounds was assessed using MDR1-MDCK cell line. The extraction ratio was calculated using A−B and B−A permeability as described elsewhere. 39 Mice plasma protein binding was determined using mouse blood and brain tissue binding was determined using rat brain tissue homogenates using rapid equilibrium dialysis approach. 40 Solubility Assay. Equilibrium solubility was determined using a miniaturized shake flask approach. 41 Aliquots of 10 mM DMSO compound solution were dispensed in triplicate in 96-well plates. The DMSO was removed using a GeneVac HT4X evaporator for approximately 1.5 h. Media (pH 6.8 phosphate buffer) was added to each well to achieve a target concentration of 0.75−1 mM. The plate was sealed and shaken for a minimum of 16 h, then centrifuged for phase separation. The supernatant was centrifuged a second time in a new plate. Finally, an aliquot of the second supernatant was transferred to another plate for further dilution and subsequent analysis. Quantification of solubility was performed using high-performance liquid chromatography and MS/MS using a standard calibration curve prepared from the original DMSO compound solution. Experimental variability was determined from different days and experimentalists, with a log standard deviation of 0. 25